Method for smart gas pipeline network inspection and internet of things system thereof

ABSTRACT

The embodiments of the present disclosure provide a method for smart gas pipeline network inspection, implemented on a smart gas pipeline network security management platform based on an Internet of Things system for smart gas pipeline network inspection, and the method comprising: obtaining a gas pipeline network distribution; determining at least one inspection sub-area based on the gas pipeline network distribution; determining, based on the at least one inspection sub-area, an inspection plan for each of the at least one inspection sub-area, the inspection plan at least including an inspection frequency.

CROSS-REFERENCE TO RELATED DISCLOSURES

This application claims the priority of the Chinese application No.202211616065.X filed on Dec. 15, 2022, the entire content of which isincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the technical field of gas pipelinenetwork inspection, in particular to a method for smart gas pipelinenetwork inspection and an Internet of Things system thereof.

BACKGROUND

Damage to a gas pipeline network will not only cause economic losses toa gas company but also affect lives of urban residents, and eventhreaten personal safety of people in severe cases. Therefore, the gascompany needs to arrange inspection personnel to inspect the gaspipeline network to find problems in time and solve the problems as soonas possible. However, due to a large coverage area of the gas pipelinenetwork in the city, it is easy to cause missed inspections andre-inspections during inspections by the inspection personnel, resultingin low inspection efficiency and poor inspection results.

On this basis, it is hoped that a method for smart gas pipeline networkinspection and an Internet of Things system thereof can be provided toimprove the inspection efficiency and inspection result.

SUMMARY

The embodiments of the present disclosure provide a method for smart gaspipeline network inspection, implemented based on an Internet of Thingssystem for smart gas pipeline network inspection. The method comprises:obtaining a gas pipeline network distribution; determining at least oneinspection sub-area based on the gas pipeline network distribution;determining, based on the at least one inspection sub-area, aninspection plan for each of the at least one inspection sub-area, theinspection plan at least including an inspection frequency.

One of the embodiments of the present disclosure provides an Internet ofThings system for smart gas pipeline network inspection, the systemincludes: a smart gas user platform, a smart gas service platform, asmart gas pipeline network security management platform, a smart gassensor network platform, and a smart gas sensor network platform. gasobject platform; wherein the smart gas object platform is configured toobtain a gas pipeline network distribution, and transmit the gaspipeline network distribution to the smart gas pipeline network securitymanagement platform through the smart gas sensor network platform; thesmart gas pipeline network security management platform is configured todetermine at least one inspection sub-area based on the gas pipelinenetwork distribution; determine, based on the at least one inspectionsub-area, an inspection plan for each of the at least one inspectionsub-area, inspection plan including at least inspection frequency; andthe smart gas service platform is configured to feed back the inspectionplan to the smart gas user platform.

One of the embodiments of the present disclosure provides anon-transitory computer-readable storage medium storing computerinstructions, wherein after reading the computer instructions in thestorage medium, a computer executes the method for smart gas pipelinenetwork inspection according to the above descriptions.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram of an Internet of Things system for smartgas pipeline network inspection according to some embodiments of thepresent disclosure;

FIG. 2 is a flowchart illustrating an exemplary method for smart gaspipeline network inspection according to some embodiments of the presentdisclosure;

FIG. 3 is a flowchart illustrating an exemplary process for determiningat least one inspection personnel station according to some embodimentsof the present disclosure;

FIG. 4 is a flowchart illustrating an exemplary process for determiningat least one inspection sub-area according to some embodiments of thepresent disclosure; and

FIG. 5 is a schematic diagram illustrating an exemplary process fordetermining an inspection priority value of an inspection pointaccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The technical solutions of the present disclosure embodiments will bemore clearly described below, and the accompanying drawings need to beconfigured in the description of the embodiments will be brieflydescribed below. Obviously, drawings described below are only someexamples or embodiments of the present disclosure. Those skilled in theart, without further creative efforts, may apply the present disclosureto other similar scenarios according to these drawings. Unless obviouslyobtained from the context or the context illustrates otherwise, the samenumeral in the drawings refers to the same structure or operation.

It should be understood that the “system”, “device”, “unit”, and/or“module” used herein are one method to distinguish different components,elements, parts, sections, or assemblies of different levels inascending order. However, the terms may be displaced by otherexpressions if they may achieve the same purpose.

As shown in the present disclosure and claims, unless the contextclearly prompts the exception, “a”, “one”, and/or “the” is notspecifically singular, and the plural may be included. It will befurther understood that the terms “comprise,” “comprises,” and/or“comprising,” “include,” “includes,” and/or “including,” when used inthe present disclosure, specify the presence of stated steps andelements, but do not preclude the presence or addition of one or moreother steps and elements thereof.

The flowcharts are used in present disclosure to illustrate theoperations performed by the system according to the embodiment of thepresent disclosure. It should be understood that the front or rearoperation is not necessarily performed in order to accurately. Instead,the operations may be processed in reverse order or simultaneously.Moreover, one or more other operations may be added to the flowcharts.One or more operations may be removed from the flowcharts.

FIG. 1 is a schematic diagram of an Internet of Things system 100 forsmart gas pipeline network inspection according to some embodiments ofthe present disclosure. The Internet of Things system 100 for smart gaspipeline network inspection involved in the embodiments of the presentdisclosure will be described in detail below. It should be noted thatthe following embodiments are only used to explain the presentdisclosure, and do not constitute a limitation on the presentdisclosure.

As shown in FIG. 1 , the Internet of Things system 100 for smart gaspipeline network inspection may include a smart gas user platform 110, asmart gas service platform 120, a smart gas pipeline network securitymanagement platform 130, a smart gas sensor network platform 140, and asmart gas object platform 150.

The smart gas user platform 110 may be a user-led platform used toobtain user requirements and feed back information to users. In someembodiments, the smart gas user platform 110 may be configured as aterminal device. For example, the smart gas user platform 110 may be anintelligent electronic device that realizes data processing and datacommunication, such as a desktop computer, a tablet computer, a laptopcomputer, a mobile phone, etc.

In some embodiments, the smart gas user platform 110 may include a gasuser sub-platform and a supervision user sub-platform. The gas usersub-platform is oriented to gas users, and may be used to receivegas-related data and gas pipeline network inspection reminderinformation (such as an inspection time, etc.) sent by a smart gasconsumption service sub-platform, and send gas-related data queryinstructions to the smart gas consumption service sub-platform. Thesupervision user sub-platform is oriented to supervision users (forexample, users in a safety supervision department), and may be used toreceive gas pipeline network inspection management information (such asan inspection plan, etc.) sent by a smart supervision servicesub-platform, and send gas pipeline network inspection managementinformation query instructions to the smart supervision servicesub-platform.

The smart gas service platform 120 may be a platform for receiving andtransmitting data and/or information. For example, the smart gas serviceplatform 120 may be configured to receive the gas pipeline networkinspection management information uploaded by a smart gas data center132 of the smart gas pipeline network security management platform 130,and send the gas pipeline network inspection management information tothe smart gas user platform 110. In some embodiments, the smart gasservice platform 120 may be configured to receive query instructions(e.g., the gas-related data query instructions, the gas pipeline networkinspection management information query instructions, etc.) issued bythe smart gas user platform 110, and send the query instructions to thesmart gas data center 132 of the smart gas pipeline network securitymanagement platform 130.

In some embodiments, the smart gas service platform 120 may include thesmart gas consumption service sub-platform and the smart supervisionservice sub-platform. The smart gas consumption service sub-platform maybe used to receive the gas-related data and the gas pipeline networkinspection reminder information uploaded by the smart gas data center132, and send the gas-related data and the gas pipeline networkinspection reminder information to the gas user sub-platform. The smartgas consumption service sub-platform may also be used to receive thegas-related data query instructions issued by the gas user sub-platform,and send the gas-related data query instructions to the smart gas datacenter 132. The smart supervision service sub-platform may be used toreceive the gas pipeline network inspection management informationuploaded by the smart gas data center 132, and transmit the gas pipelinenetwork inspection management information to the supervision usersub-platform. The smart supervision service sub-platform may also beused to receive the gas pipeline network inspection managementinformation query instructions sent by the supervision usersub-platform, and send the gas pipeline network inspection managementinformation query instructions to the smart gas data center 132.

The smart gas pipeline network security management platform 130 mayrefer to a platform that coordinates and harmonizes the connection andcooperation between various functional platforms, gathers allinformation of the Internet of Things, and provides functions ofperception management and control management for the Internet of Thingsoperation system. For example, the smart gas pipeline network securitymanagement platform 130 may be configured to receive relevant data(e.g., a gas pipeline network distribution, pipeline features, etc.) ofpipeline network devices sent by the smart gas sensor network platform140, perform inspection management on the gas pipeline network, anddetermine the inspection plan.

In some embodiments, the smart gas pipeline network security managementplatform 130 may include a smart gas pipeline network inspectionmanagement sub-platform 131 and a smart gas data center 132. The smartgas pipeline network inspection management sub-platform 131 may includean inspection plan management module, an inspection time warning module,an inspection status management module, and an inspection problemmanagement module.

The inspection plan management module may be used to set and adjust theinspection plan of the pipeline network device, and send the inspectionplan based on the smart gas data center 132 through the smart gaspipeline network inspection engineering sensor network sub-platform tothe inspection engineering object sub-platform. The inspection planmanagement module may also send the inspection plan that may affect gasconsumption of the users to the gas user sub-platform through the smartgas consumption service sub-platform through the smart gas data center132.

The inspection time management module may be used to automaticallyarrange the inspection plan that not be executed according to theinspection time, and prompt and alarm based on a preset time threshold.In some embodiments, the management personnel may directly generate aninspection reminder instruction through the inspection time warningmodule, and send the inspection reminder instruction to the smart gaspipeline network inspection engineering object sub-platform through thesmart gas pipeline network inspection engineering sensor networksub-platform based on the smart gas data center 132.

The inspection status management module may be used to check theimplementation of the inspection plan for the pipeline network deviceand the implementation of a historical inspection plan.

The inspection problem management module may be used to manageinspection, remote processing, and message sending of inspectionproblems.

In some embodiments, the smart gas data center 132 may be configured toreceive information such as the gas pipeline network distribution, thepipeline features, and other information uploaded by the smart gassensor network platform 140, and send the data to the smart gas pipelinenetwork security management sub-platform for analysis and processing.When processed by the smart gas pipeline network security managementsub-platform, the data may be sent back to the smart gas data center132, and the smart gas data center 132 may summarize and store theprocessed data and upload the processed data to the smart gas serviceplatform 120. The processed data then may be transmitted to the smartgas user platform 110 via the smart gas service platform 120. In someembodiments, the smart gas data center 132 may also be configured toreceive query instructions (e.g., the gas-related data queryinstructions, the gas pipeline network inspection management informationquery instructions, etc.) issued by the smart gas service platform 120,and send the query instructions to the smart gas sensor network platform140.

The smart gas sensor network platform 140 may refer to a platform forprocessing, storing and transmitting data and/or information. Forexample, the smart gas sensor network platform 140 may be configured toreceive data and/or information obtained by the smart gas objectplatform 150, such as the gas pipeline network distribution, pipelinefeatures, etc., and transmit the data and/or information to the smartgas data center 132. In some embodiments, the smart gas sensor networkplatform 140 may be configured as a communication network and gateway.

In some embodiments, the smart gas sensor network platform 140 mayinclude a smart gas pipeline network device sensor network sub-platformand a smart gas pipeline network inspection engineering sensor networksub-platform. The smart gas pipeline network device sensor networksub-platform may be used to receive relevant data of the pipelinenetwork device, such as the gas pipeline network distribution, pipelinefeatures, etc., and send the relevant data to the smart gas data center132. The smart gas pipeline network device sensor network sub-platformmay also be used to receive relevant data query instructions of thepipeline network device issued by the smart gas data center 132, andsend the relevant data query instructions to the smart gas pipelinenetwork device object sub-platform. The smart gas pipeline networkinspection engineering sensor sub-platform may be used to receive theinspection-related information (such as the inspection plan, inspectionreminder instructions, etc.) issued by the smart gas data center 132,and send the inspection-related information to the smart gas pipelinenetwork inspection engineering object sub-platform. The smart gaspipeline network inspection engineering sensor sub-platform may also beused to receive the execution feedback of the inspection-relatedinformation uploaded by the smart gas pipeline network inspectionengineering object sub-platform, and send the execution feedback to thesmart gas data center 132.

The smart gas object platform 150 may be a functional platform forobtaining data and/or information related to pipeline network objects.For example, the smart gas object platform 150 may be used to obtainrelevant data of the pipeline network device, and transmit the relevantdata to the smart gas data center 132 through the smart gas sensornetwork platform 140. In some embodiments, the smart gas object platform150 may be configured as various types of devices, such as pipelinenetwork devices (e.g., pipelines, gate stations, etc.) and inspectionengineering-related devices (e.g., alarm device, etc.).

In some embodiments, the smart gas object platform 150 may include asmart gas pipeline network device object sub-platform and a smart gaspipeline network inspection engineering object sub-platform. The smartgas network object sub-platform may be used to receive the relevant dataquery instruction of the pipeline network device transmitted by thesmart gas sensor network platform 140. After obtaining the relevant dataof the pipeline network device, the smart gas network objectsub-platform may be used to upload the relevant data to the smart gasdata center 132 through the smart gas sensor network platform 140. Thesmart gas network inspection engineering object sub-platform may be usedto receive the inspection-related information transmitted by the smartgas sensor network platform 140, perform a corresponding inspectionoperation on the pipeline network device, and feed back an executionresult to the smart gas data center 132 through the smart gas sensornetwork platform 140.

In some embodiments of the present disclosure, the Internet of Thingssystem for smart gas pipeline network inspection may be constructedthrough the smart gas user platform, the smart gas service platform, thesmart gas pipeline network security management platform, the smart gassensor network platform, and the smart gas object platform, which mayform a closed loop of smart gas pipeline network inspection managementinformation operation among the gas pipeline network device, pipelinenetwork inspection personnel, gas operators and gas users, therebyrealizing the informatization and intelligence of the pipeline networkinspection management and making the management more efficient.

It should be noted that the above descriptions of the Internet of Thingssystem 100 for smart gas pipeline network inspection and its variouscomponents may be only for the convenience of description, and may notlimit the description to the scope of the illustrated embodiments. Itmay be understood that for those skilled in the art, after understandingthe principle of the device, various components may be combinedarbitrarily, or a subsystem may be formed to connect with othercomponents without departing from the principle.

FIG. 2 is a flowchart illustrating an exemplary method for smart gaspipeline network inspection according to some embodiments of the presentdisclosure.

In some embodiments, process 200 may be executed by the smart gaspipeline network security management platform 130. As shown in FIG. 2 ,process 200 may include the following steps.

Step 210, obtaining a gas pipeline network distribution.

The gas pipeline network distribution may refer to distribution data ofthe gas pipeline network, such as a coverage area of the gas pipelinenetwork, node locations of the gas pipeline network, and a number ofnodes of the gas pipeline network.

The gas pipeline network distribution may be obtained based on variousmanners. In some embodiments, the smart gas data center of the smart gaspipeline network security management platform 130 may obtain the gaspipeline network distribution based on the smart gas pipeline networkdevice object sub-platform of the smart gas object platform. The smartgas pipeline network device object sub-platform may be configured with amonitoring device for the pipeline network device to obtain the pipelinenetwork distribution. For example, the smart gas pipeline network deviceobject sub-platform may use a Global Positioning System (GPS) to obtaininformation such as the coverage area of the gas pipeline network andthe node locations of the gas pipeline network, etc.

Step 220, determining at least one inspection sub-area based on the gaspipeline network distribution.

The inspection sub-area may refer to any area in an area where the gaspipeline network is located. For example, if distribution areas of thegas pipeline network include area A, area B, and area C, these threeareas may be considered as inspection sub-areas.

The inspection sub-area may be determined in various ways. In someembodiments, the inspection sub-areas may be divided according to apreset rule, for example, according to physical areas (e.g., citydivisions). The preset rule may be a preset division rule, and may bedetermined based on historical experience, algorithms, or the like.

In some embodiments, at least one inspection personnel station may alsobe determined based on the gas pipeline network distribution, and thenat least one inspection sub-area may be determined based on the gaspipeline network distribution and the at least one inspection personnelstation.

The inspection personnel station may refer to a parking point and astarting point of the inspection personnel during the inspection. Theinspection personnel station may be a gas pipeline network node (such asa gate station, a gas storage station, etc.), or a site set up on anylocation of the pipeline of the gas pipeline network (such as a siteestablished at the midpoint of the pipeline, or the like).

The inspection personnel station may be determined in various ways. Insome embodiments, based on the gas pipeline network distribution,several inspection personnel stations may be randomly generatedaccording to preset requirements. The preset requirements may be presetrequirements. For example, the preset requirements may be that adistance between two pipeline network nodes being greater than a presetdistance threshold, or the like. Specifically, based on the gas pipelinenetwork distribution, a certain pipeline network node may be used as abase point, and points whose distances from the pipeline network nodeare greater than the preset distance threshold may be used as a firstbatch of inspection stations. It should be noted that a relativedistance between any two inspection stations in the determined firstbatch of inspection stations needs to be not less than the presetdistance threshold. Then the first batch of inspection stations may betaken as the base points, and other points whose distance is greaterthan the preset distance threshold may be taken as the second batch ofinspection stations. Proceed in this way, until the inspection stationsin the gas pipeline network coverage area are determined. The base pointmay be understood as the basic point or the starting point.

In some embodiments, the first pipeline network graph may also beconstructed based on the gas pipeline network distribution. According tothe first pipeline network graph, through the probability determinationmodel, based on the nodes of the first pipeline network graph and/or theedges of the first pipeline network graph, the probability that thenodes and/or edges of the first pipeline network graph are theinspection personnel station may be output. Based on the output of thenodes of the first pipeline network graph and the edges of the firstpipeline network graph, at least one inspection personnel station may bedetermined. For the specific content of how to determine the at leastone inspection personnel station based on the gas pipeline networkdistribution by using the first pipeline network graph and theprobability determination model, please refer to FIG. 3 and relateddescriptions thereof.

In some embodiments, the inspection sub-areas may be preliminarilydivided according to a preset rule based on the gas pipeline networkdistribution, and then the inspection sub-areas may be determined incombination with the location of the inspection personnel station. Forexample, the inspection sub-areas may be preliminarily divided (e.g.,area A, B, and C) by physical area (e.g., city division), if there is noinspection personnel station in area A, but there are inspectionpersonnel stations in areas B and C, area A may be merged into an area(e.g., area C) where the nearest inspection personnel station is locatedfrom area A, then area B and (area A+ area C) may be determined as thefinal two inspection sub-areas.. It should be noted that this manner isonly used as an example, and does not limit the division manner of thepreset distance.

In some embodiments, the second pipeline network graph may also beconstructed based on the gas pipeline network distribution and at leastone inspection personnel station. According to the second pipelinenetwork graph, at least one second pipeline network sub-graph may bedetermined by a preset sub-graph segmentation manner. Then based on theat least one second pipeline network sub-graph, at least one inspectionsub-area may be determined. For the specific content of how to determinethe at least one inspection sub-area based on the gas pipeline networkdistribution and the at least one inspection personnel station using thesecond pipeline network graph and the preset sub-graph segmentationmanner, please refer to FIG. 4 and related descriptions thereof.

In some embodiments of the present disclosure, based on the gas pipelinenetwork distribution, the at least one inspection personnel station maybe determined, and then based on the gas pipeline network distributionand the at least one inspection personnel station, the at least oneinspection sub-area may be determined, which can ensure that eachinspection sub-area has at least one inspection personnel station,thereby ensuring the normal inspection of the inspection area.

Step 230, determining, based on the at least one inspection sub-area, aninspection plan for each of the at least one inspection sub-area.

The inspection plan may refer to a manner that the inspection personnelshould take when inspecting the gas pipeline network, including but notlimited to an inspection cycle, an inspection-related operation, etc.For example, when the relevant data of the pipeline network devicefluctuates abnormally, a corresponding inspection plan may be a new planobtained by extending the inspection cycle and changing the inspectionroute based on an original inspection plan. In some embodiments, theinspection plan may include at least an inspection frequency.

The inspection frequency may refer to times of inspections of a certaininspection sub-area within a certain time period. For example, theinspection frequency may be 10 times/day, 20 times/week, etc. Theinspection frequency may be determined manually and randomly, or may bedetermined based on historical data and other manner. For example, theinspection frequency may be determined based on an accident frequency ofthe gas pipeline network in the historical accident data of a certaininspection sub-area, the higher the accident frequency, the higher thecorresponding inspection frequency may be.

In some embodiments, the smart gas data center may transmit the relevantdata of the pipeline network device to the smart gas pipeline networkinspection management sub-platform, and the smart gas pipeline networkinspection management sub-platform may determine the inspection plan invarious ways. For example, the smart gas pipeline network inspectionmanagement sub-platform may match the relevant data (such as the gaspipeline network distribution, the pipeline features, etc.) of thepipeline network device in the inspection sub-area with the historicalpipeline network data, and then take the historical pipeline networkdata with the highest similarity as the reference data, and a historicalreference inspection plan corresponding to the historical pipelinenetwork data as the inspection plan in the inspection sub-area. Thehistorical pipeline network data may refer to a collection of historicalrelated data of the pipeline network device, such as a historical gaspipeline network distribution, historical pipeline features, and otherdata. The reference data may refer to the historical pipeline networkdata with the highest similarity to the related data of a currentpipeline network device. The historical reference inspection plan may bean inspection plan adopted when the pipeline network device operatesunder parameters of the reference data.

In some embodiments, the inspection plan may further include aninspection route; and the inspection sub-area may also include aninspection personnel station and at least one inspection point.

In some embodiments, it is also possible to determine a route thattraverses each of at least one inspection point from the inspectionpersonnel station in the inspection sub-area as an inspection route anddetermine the inspection plan of the inspection sub-area based on theinspection route.

The inspection point may refer to a destination of the inspection. Theinspection point may be a certain segment of the pipeline in thepipeline network, a node at an opposite end of the pipeline, or acertain point on the pipeline, etc.

Inspection points may be determined in several ways. In someembodiments, inspection points may be determined based on historicalinspection data. For example, the top 10 historical inspection pointsranked from high to low in the times of inspections in the historicalinspection data may be used as the inspection points. The historicalinspection point may refer to a pipeline network node or pipeline thatused to be the inspection point. In some embodiments, the inspectionpoint may also be determined based on an actual situation. For example,the node at the opposite end of an abnormal pipeline may be used as theinspection point. The abnormal pipeline may refer to a certain segmentof the gas pipeline network whose pipeline features exceed a presetfeature threshold, such as a pipeline whose air pressure exceeds apreset air pressure threshold, a pipeline whose maintenance times exceeda preset maintenance threshold, etc.

The inspection route may refer to a route that the inspection personnelshould walk during an inspection. For example, the inspection route maybe a route from the pipeline network node A to the pipeline network nodeB. As another example, the inspection route may be a route from thepipeline network node A, through the pipeline network node B, and thento the middle end of the pipeline C, or the like.

The inspection route may be determined based on various manners, such asrandomly determining the inspection route. In some embodiments, theinspection route may be generated according to a preset manner. Forexample, the top 10 historical inspection points ranked from high to lowin the historical inspection data may be taken as the inspection point,and the inspection point farthest from the other inspection points maybe taken as the starting point. According to a principle of shortestinspection route distance, a route formed by the other inspection pointswith a series connection may be regarded as the inspection route. Theprinciple of inspection route distance may be determined based on aprogram algorithm, or the like.

In some embodiments, each inspection point in the at least oneinspection point may have an inspection priority value; and theinspection route may be determined based on the inspection priorityvalue of the each inspection point. For the specific content of theinspection priority value and how to determine the inspection routebased on the inspection priority value of each inspection point, pleaserefer to FIG. 5 and related description thereof.

In some embodiments, an inspection plan for inspecting the sub-areas maybe determined based on the inspection route. For example, after theinspection route is determined, the inspection frequency may bedetermined according to the pipeline features corresponding to theinspection points in the inspection route, and then the inspection planmay be determined. The pipeline features may be related to the featuresof the pipeline itself, such as a diameter of the pipeline, a servicelife of the pipeline, or the like. Specifically, when it is found thatthe service life of the pipeline corresponding to a certain inspectionpoint in the inspection route exceeds an age threshold, the inspectionfrequency may be increased on the basis of the original inspection planto obtain a new inspection plan. The age threshold may refer to a presetage limit, which may be determined based on historical experience,simulation tests, and other manner.

In some embodiments of the present disclosure, determine the inspectionroute and then determine the inspection plan based on the inspectionroute, which can not only reduce the workload of the inspectionpersonnel, but also be conducive to the unified scheduling andmanagement of the inspection personnel, defining the scope ofresponsibility and saving the management cost.

In some embodiments of the present disclosure, one or more inspectionsub-areas and the inspection plan of each of the inspection sub-areasmay be determined based on the gas pipeline network distribution using apipeline network graph, a probability determination model, presetsub-graph segmentation, and other methods, which can make inspectionplan more scientific and reasonable, beneficial to improve theinspection efficiency and inspection effect, thereby finding problems intime, preventing the problems in advance or dealing with the problems assoon as possible, and reducing the losses of gas companies.

FIG. 3 is a flowchart illustrating an exemplary process for determiningat least one inspection personnel station according to some embodimentsof the present disclosure.

In some embodiments, process 300 may be executed by the smart gaspipeline network security management platform 130. As shown in FIG. 3 ,the process 300 may include the following steps.

Step 310, constructing a first pipeline network graph 312 based on thegas pipeline network distribution 311, wherein nodes of the firstpipeline network graph 312 correspond to the pipeline network branchesin the gas pipeline network distribution 311; edges of the firstpipeline network graph 312 correspond to the pipelines in the gaspipeline network distribution 311; and each edge of the first pipelinenetwork graph corresponds to a pipeline connecting two network pipelinebranches.

The first pipeline network graph 312 may refer to a graph determinedbased on the information of the gas pipeline network distribution 311,which may represent the distribution information of the gas pipelines,gate stations, gas storage stations, and other pipeline network devicesin various areas of the city in the gas pipeline network distribution311. In some embodiments, the smart gas pipeline network securitymanagement platform 130 may obtain the information of the gas pipelinenetwork distribution 311 from the smart gas data center 132 based on thesmart gas pipeline network inspection management sub-platform toconstruct the first pipeline network graph 312.

In some embodiments, the smart gas pipeline network security managementplatform 130 may construct the first pipeline network graph 312 based onthe gas pipeline network distribution 311.

The nodes of the first pipeline network graph 312 may be used torepresent pipeline network branches in the gas pipeline networkdistribution 311, for example, the inflection point of the gas pipeline,the gate station, the gas storage station, etc. The nodes of the firstpipeline network graph 312 may be determined according to a preset noderule. For example, the nodes of the first pipeline network graph 312 maybe determined according to a number, density, and importance of thepipeline inflection points, gate stations, and gas storage stations inthe gas pipeline network distribution 311 and an area to which thepipeline inflection points, gate stations, and gas storage stationsbelong, or may be preset based on experience.

In some embodiments, the nodes in the first pipeline network graph 312may include a node n1, a node n2, a node n12, or the like.

The features of the node of the first network graph 312 may includevarious information. In some embodiments, the features of node of thefirst pipeline network graph 312 may include a node type correspondingto the node, and the node type may include a plurality of types, such asa gate station type, a gas storage station type, a pipeline inflectionpoint type, or the like. The features of the node of the first pipelinenetwork graph 312 may also include other information about thecorresponding node, such as times of historical inspections, a failurerate, or the like. In some embodiments, the features of the node of thefirst pipeline network graph 312 may further include whether the node isan inspection personnel station. For the specific content of theinspection personnel station, please refer to FIG. 2 and relateddescriptions thereof.

The edges of the first network graph 312 may be used to represent thepipelines in the gas network distribution 311. In some embodiments, theedges of the first pipeline network graph 312 may connect two nodes ofthe first pipeline network graph 312, which may represent a relationshipbetween the two nodes of the first pipeline network graph 312, forexample, neighbor relationship, distance relationship, etc. It may beunderstood that, there may be a plurality of gas pipelines between thetwo nodes (e.g., two gas storage stations) of the first pipeline networkgraph 312. Merely by way of example, a certain gas storage station has aplurality of gas pipelines (e.g., 2 pipelines, 3 pipelines) laid atdifferent angles or directions, and the plurality of gas pipelinesconverge at a same gas storage station. In this case, in the firstpipeline network graph 312, there may be a plurality of edges (e.g., 2edges, 3 edges) connected between the two nodes corresponding to the twogas storage stations according to the number of gas pipelines.

In some embodiments, the edges in the first pipeline network graph 312may include edge A, edge B, edge Q, or the like. In some embodiments,the edges of the first pipeline network graph 312 may be directed edges,and the directions of the edges may indicate the transmission directionsof the gas. For example, edge A may represent that gas is transmittedfrom node n1 to node n2.

The edge features of the first pipeline network graph 312 may includevarious information. In some embodiments, the edge features of the firstpipeline network graph 312 may include the length of the gas pipelinecorresponding to the edge, for example, 50 m. The edge features of thefirst pipeline network graph 312 may also include other information,such as the usage duration of the gas pipeline, the times of historicalinspections, the failure rate, or the like. In some embodiments, theedge features of the first pipeline network graph 312 may furtherinclude whether the edge is an inspection personnel station.

Step 320, outputting, based on the nodes of the first pipeline networkgraph 312 and/or the edges of the first pipeline network graph 312through a probability determination model 321, a probability 322 thatthe nodes of the first pipeline network graph 312 and/or the edges ofthe first pipeline network graph 312 are the inspection personnelstation.

The inspection personnel station may refer to a place where theinspection personnel may park, and the inspection personnel may startfrom a certain inspection personnel station to carry out the inspectionof the gas pipeline. The inspection personnel station may be set at thegate station, gas storage station, or near the pipeline where the gaspipeline network is distributed. In some embodiments, the inspectionpersonnel station may correspond to a node or edge of the first pipelinenetwork graph, that is, the plurality of nodes or edges of the firstpipeline network graph 312 may be set as inspection personnel stations.It should be noted that, when a certain edge of the first pipelinenetwork graph 312 is set as an inspection personnel inspection point,the inspection personnel inspection point may be represented based on amidpoint of the edge or in other forms.

In some embodiments, the inspection personnel station may be determinedbased on a preset requirement. For example, at least one inspectionpersonnel station may be randomly determined, and the at least oneinspection personnel station is used as a dot. The node or edge which isoutside the preset radius threshold (for example, 200 m) correspondingthe dot and closest to the at least one inspection personnel station maybe selected as the new inspection personnel station.

The probability that each node or each edge in the first pipelinenetwork graph 312 is set as the inspection personnel station may bedifferent. In some embodiments, the smart gas pipeline network securitymanagement platform 130 may determine the probability that each node oreach edge in the first pipeline network graph 312 is set as theinspection personnel station through the probability determination model321.

The probability determination model 321 may refer to a model fordetermining the probability that the node and/or the edge of the firstpipeline network graph is the inspection personnel station. In someembodiments, the probability determination model 321 may be a trainedmachine learning model. For example, the probability determination model321 may include a recurrent neural network model, a convolutional neuralnetwork, or other custom model structures, or the like, or anycombination thereof.

In some embodiments, the probability determination model 321 may be atrained graph neural network model. As shown in FIG. 3 , the smart gaspipeline network security management platform 130 may input the firstpipeline network graph 312 into the probability determination model 321,and process the first pipeline network graph 312 through the probabilitydetermination model 321, and obtain the probability 322 that each nodeand/or each edge of the first pipeline network graph is the inspectionpersonnel station outputted by the probability determination model 321based on the nodes and/or edges of the first pipeline network graph 312.For example, the probability determination model may output theprobability that node n1 is the inspection personnel station based onthe node n1, output the probability that node n3 is the inspectionpersonnel station based on the node n3, output the probability that edgeQ is the inspection personnel station based on the edge Q, etc.

In some embodiments, the probability determination model 321 may beobtained by training a plurality of sample pipeline network graphs withlabels. The sample pipeline network graphs may be a plurality ofhistorical pipeline network graphs, and the labels may be determinedbased on whether the nodes or edges in the sample pipeline networkgraphs are historical inspection personnel stations. For example, if anode or edge is a historical inspection personnel station, the value ofthe label corresponding to the node or edge is 1, otherwise it is 0,etc. In some embodiments, the labels may also be determined based on thehistorical inspection information of the nodes or edges in the samplepipeline network graphs. For example, the lower the failure rate of thehistorical inspection, the higher the probability of the node or edgebeing determined as the inspection personnel station. The labels may belabeled manually, or the like.

When training the initial probability determination model, the smart gaspipeline network security management platform 130 may input each samplepipeline network graph into the probability determination model, andthrough the processing of the probability determination model, andobtain the probability value of each node and edge as the inspectionpersonnel station outputted by the probability determination model basedon the each node and edge in the sample pipeline network graph. Thesmart gas pipeline network security management platform 130 mayconstruct a loss function based on the label of each sample pipelinenetwork graph and the output of the probability determination model, anditeratively update the parameters of the probability determination modelbased on the loss function, and obtain a trained probabilitydetermination model until the preset conditions are satisfied and thetraining is completed. The preset conditions may be that the lossfunction is less than a threshold, convergence, or the training cyclereaches a threshold.

In some embodiments, the label setting manner may also be: in the samplepipeline network graph, the label of the node or edge that is actuallyset as the inspection personnel station may be set to 1, and the labelvalues of other nodes/edges are set based on a preset attenuationdegree. For example, the label values of other nodes/edges may be set tovalues in a range of [0,1].

For example, the label values of the other nodes/edges may be determinedbased on a label value algorithm of L=1-k^(∗)degree, where, k representsthe preset attenuation degree (for example, 0.1), and degree representsa neighbor degree or distance (for example, 1, 2) of the other nodes oredges and the inspection personnel station. Exemplarily, if a node isadjacent to the inspection personnel station (the neighbor degree is 1),the label value of the node may be L=1-0.1^(∗)1=0.9.

In some embodiments, if a certain node or edge obtains a plurality ofnode labels based on different inspection personnel stations, a finallabel value of the node or edge may be determined based on thecorresponding plurality of node labels, such as the final label valuemay be an average value or a weighted sum of the plurality of nodelabels, etc.

In some embodiments of the present disclosure, due to the large numberof the nodes and edges, the training efficiency of the probabilitydetermination model may be improved by setting the label when trainingthe probability determination model by the attenuation degree.

Step 330, determining at least one inspection personnel station based onan output of the nodes of the first pipeline network graph 312 and theedges of the first pipeline network graph 312.

In some embodiments, after the first pipeline network graph 312 isprocessed by the probability determination model 321, the smart gaspipeline network security management platform 130 may determine the atleast one inspection personnel station according to preset rules basedon the probability values of the inspection personnel stations outputbased on the nodes and edges of the first pipeline network graph. Merelyby way of example, X (e.g., 5 or 10) nodes and/or edges of theinspection personnel stations with the largest probability value may beused as the inspection personnel stations, wherein the distance betweenthese X nodes and/or edges may be larger than the preset distancethreshold, so that the distribution of inspection personnel station maybe more uniform.

In some embodiments, the smart gas pipeline network security managementplatform 130 may sort (e.g., sort in a descending order) based on theprobability values of inspection personnel stations, select preset top Mnodes/edges as candidate inspection personnel stations, and randomlyselect N candidate inspection personnel stations that satisfy a presetconstraint condition as the target inspection personnel stations amongthe M candidate inspection personnel station. The preset constraintcondition may be that among the N candidate inspection personnelstations, the distance (e.g., the length of the gas pipeline) betweenany two candidate inspection personnel stations is greater than a presetdistance threshold (e.g., 200 m).

As shown in FIG. 3 , after the probability determination model 321processed the first pipeline network graph 312 and output probabilityvalues of the inspection personnel station based on the nodes and edgesof the first pipeline network graph 312, the probability values may besorted in descending order, and three inspection personnel stationsmeeting the preset constraint conditions may be determined as node n1,node n9, and node n12. Descriptions here are examples only, and are notintended to be limiting. For example, the inspection personnel stationsmay also be 4, 5, etc.

The number N of inspection personnel stations may be determined based onactual needs or in various suitable ways. For example, the number ofinspection personnel stations may be determined based on the proportion(e.g., 10%) of the total number of the nodes and edges in the firstpipeline network graph 312, the number of divided areas of the gaspipeline network distribution 311, the number of actual inspectionpersonnel, or the like, or any combination thereof. For example, if thenumber of the actual inspection personnel is 20, the number of theinspection personnel stations may be set to be less than 20 (e.g., 18).

In some embodiments, the number of the inspection personnel stations maybe determined based on an average value of the sub-graph complexity ofthe plurality of sub-graphs after the pipeline network sub-graph isdivided. The pipeline network sub-graph corresponds to the inspectionarea, and the greater the complexity of the sub-graph, the larger thenumber of nodes/edges representing the sub-graph. It may be understoodthat the greater the complexity of each sub-graph in the plurality ofsub-graphs, the larger the average value of the sub-graph complexity ofall the sub-graphs correspondingly, and the greater the inspectionworkload of the inspection personnel.

In some embodiments, an average threshold value may be preset, and thesmart gas pipeline network security management platform 130 maydetermine the corresponding number of the inspection personnel stationsby increasing the number of the inspection personnel stations to makethe average value of the sub-graph complexity in the plurality ofsub-graphs lower than the preset average value threshold. For example,the number of current inspection personnel stations is 3, and the numberof nodes and edges of each sub-graph in the determined 3 sub-graphs isrelatively large (for example, exceeding a preset number threshold),that is, the average value of the sub-graph complexity is large. At thistime, the number of the current inspection personnel stations mayincrease (for example, add one in total), then the number of sub-graphsmay increase (for example, add one in total). At the same time, thenumber of nodes and edges of each sub-graph may decreasecorrespondingly, that is, the sub-graph complexity of each sub-graph maydecrease correspondingly. When the average value of sub-graph complexityof all sub-graphs is less than the preset average threshold or the sumof the number of nodes and edges of each sub-graph is less than thepreset number threshold, the number of the current inspection personnelstations may stop increasing, and the number of the current inspectionpersonnel stations may be determined as the final number of inspectionpersonnel stations.

For more information on the pipeline network sub-graph and the sub-graphcomplexity, please refer to FIG. 4 and related descriptions thereof.

In some embodiments of the present disclosure, setting the number of theinspection personnel stations in combination with the workload ofinspection can effectively reduce the pressure of inspection, and makethe method for determining the inspection personnel stations moreuser-friendly.

In some embodiments of the present disclosure, the probability of theinspection personnel station may be determined by the probabilitydetermination model, and then the inspection personnel station may bedetermined based on the probability of the inspection personnel station,which can make the result more accurate, and improve the determinationefficiency of the inspection personnel station at the same time.

FIG. 4 is a flowchart illustrating an exemplary process for determiningat least one inspection sub-area according to some embodiments of thepresent disclosure.

In some embodiments, process 400 may be performed by the smart gaspipeline network security management platform 130. As shown in FIG. 4 ,process 400 may include the following steps.

Step 410, constructing a second pipeline network graph 413 based on thegas pipeline network distribution 411 and the at least one inspectionpersonnel station 412.

The second pipeline network graph 413 may refer to a graph determinedbased on the gas pipeline network distribution 411 and the informationof at least one inspection personnel station 412, which may representthe distribution information of the gas pipelines, gate stations, gasstorage stations, and other pipeline network devices in the gas pipelinenetwork distribution and the inspection personnel stations in variousareas of the city. In some embodiments, the smart gas pipeline networksecurity management platform 130 may obtain the information of the gaspipeline network distribution 411 and the at least one inspectionpersonnel station 412 from the smart gas data center 132 based on thesmart gas pipeline network inspection management sub-platform todetermine the second pipeline network graph 413.

In some embodiments, the smart gas pipeline network security managementplatform 130 may construct the second pipeline network graph 413 basedon the gas pipeline network distribution 411 and the at least oneinspection personnel station 412. The second pipeline network graph 413may include inspection personnel stations (i.e., solid nodes): node n1,node n9, and node n12, non-inspection personnel stations (e.g., hollownodes): node n2, node n3, node n4, etc., and edges: edge A, edge B, edgeC, etc. Please refer to FIG. 3 for features of nodes and edges.

It should be noted that when the inspection personnel station is anedge, the inspection personnel station may be set as the midpoint oreach of other preset points of the edge, among the features of theinspection personnel station, the station type may be set to “gaspipeline”, and other features of the original edge may be set as thefeatures (such as historical inspection times, failure rate, etc.) ofthe inspection personnel station. In addition, the original edge may bedivided into two edges, and the features of the two edges may alsochange accordingly. For example, if the inspection personnel station isset as the midpoint of an edge, the length value in the features of theoriginal edge is L, then the length value in the features of the twodivided edges may be adjusted to L^(∗)½.

Step 420, determining at least one second pipeline network sub-graph bya preset sub-graph segmentation manner based on the second pipelinenetwork graph 413.

The second pipeline network sub-graph may refer to a graph composed ofat least some nodes and/or edges in the second pipeline network graph413.

In some embodiments, the smart gas pipeline network security managementplatform 130 may determine at least one second pipeline networksub-graph of the second pipeline network graph 413 based on the physicalplanning or administrative planning of the city area. For example, ifthe city includes area A, area B, area C, etc., the smart gas pipelinenetwork security management platform 130 may correspondingly divide thesecond pipeline network graph 413 into three second pipeline networksub-graphs including a second pipeline network sub-graph correspondingto the A area, a second pipeline network sub-graph corresponding to theB area, and a second pipeline network sub-graph corresponding to the Carea.

In some embodiments, the smart gas pipeline network security managementplatform 130 may perform a segmentation processing on the secondpipeline network graph 413 based on a preset sub-graph segmentationmanner to determine the at least one second pipeline network sub-graph.

In some embodiments, the smart gas pipeline network security managementplatform 130 may perform a plurality of rounds of iterative segmentationprocessing on the second pipeline network graph 413 based on the presetsub-graph segmentation manner, and finally determine at least one secondpipeline network sub-graph. As shown in FIG. 4 , the finally determinedsecond pipeline network sub-graph may be a sub-graph 421, a sub-graph422, and a sub-graph 423. The smart gas pipeline network securitymanagement platform 130 may be provided with an iteration counter forrecording the time or iteration round including a current iteration, andrecording nodes and/or edges divided into the second pipeline networksub-graph in each round of iterations. For example, the time/iterationround of tis 2 when a certain node is divided into a certain secondpipeline network sub-graph. Each round of iterative processing mayinclude the following contents.

The preset sub-graph segmentation manner may include the following stepsS1 to S5.

Step S1, determining at least one initial second pipeline networksub-graph based on the station nodes of the second pipeline networkgraph 413, wherein each of the at least one initial second pipelinenetwork sub-graph includes a station node.

The initial second pipeline network sub-graph may refer to a secondpipeline network sub-graph obtained when the second pipeline networkgraph 413 is segmented in each iteration. In some embodiments, the smartgas pipeline network security management platform 130 may use eachinspection personnel station in the at least one inspection personnelstation 412 as a start node or base node of the corresponding at leastone initial second pipeline network sub-graph.

As shown in FIG. 4 , the station nodes in the second pipeline networkgraph 413 include node n1, node n9, and node n12, that is, three initialsecond pipeline network sub-graphs may be determined, and each initialsecond pipe network sub-graph at least includes a corresponding stationnode. For example, the sub-graph 421 includes the station node n9. Itmay be understood that, the number of inspection personnel stations maydetermine the number of initial second pipeline network sub-graphs. Forexample, the above-mentioned three inspection personnel stations maydetermine that the number of initial second pipe network sub-graphs maybe three.

Step S2, using the pipeline network nodes of the second pipeline networkgraph 413 as nodes to be allocated, and selecting a target node from thenodes to be allocated based on a preset screening manner.

The nodes to be allocated may refer to nodes of the second pipelinenetwork sub-graph that have not been divided into the initial secondpipeline network sub-graph.

The target node may refer to a node to be allocated that is selected tobe divided into the initial second pipeline network sub-graph.

In some embodiments, the smart gas pipeline network security managementplatform 130 may select at least one node from the nodes to be allocatedas a target node based on a preset strategy. For example, several nodesto be allocated near the inspection personnel station (for example, lessthan a preset distance threshold) may be selected as target nodes basedon a random selection strategy.

In some embodiments, the smart gas pipeline network security managementplatform 130 may select the target node from the nodes to be allocatedbased on a preset screening manner.

The preset screening manner may be to select the target node based onthe current priority values of the nodes to be allocated. The currentpriority value is related to a first distance between the node and thesub-graph with a smallest current sub-graph complexity and related to asecond distance between the previous target node and the node.

The priority value may be used to determine a probability value that thenode to be allocated is selected as the target node. The priority valuemay be a value in the interval [0, 1], e.g., 0.8. The larger thepriority value, the more preferentially to determine the initial secondpipeline network sub-graph to which the node to be allocated belongs.The priority value may also be in other representations, such as 1stgrade, 2nd grade, 3rd grade, etc. In some embodiments, the smart gaspipeline network security management platform 130 may determine thepriority value of each node to be allocated based on the first distancebetween the node to be allocated and the sub-graph with a smallestcurrent sub-graph complexity and the second distance between theprevious target node and the node to be allocated.

The sub-graph complexity may refer to the complexity of the currentinitial second pipeline network sub-graph. The sub-graph complexity maybe a number, such as 0.8, or 5. The larger the number, the higher thesub-graph complexity. The sub-graph complexity may be determined basedon the number of nodes and edges of the current initial second pipelinenetwork sub-graph. Merely by way of example, the sub-graph complexitymay be equal to the sum of the number of nodes and edges of thesub-graph.

In some embodiments, the first distance may be determined based on thedistance between the current node to be allocated and the base node(i.e., the inspection personnel station of the initial second pipenetwork sub-graph) of the initial second pipe network sub-graph with thesmallest current sub-graph complexity. For example, the first distancemay be determined based on the sum of the lengths of the edgescorresponding to the shortest path connecting the current node to beallocated and the base node.

As shown in FIG. 4 , if the initial second pipe network sub-graph withthe smallest complexity of the current sub-graph is sub-graph 421, thefirst distance between the node n5 to be allocated and the base node n9of the sub-graph 421 may be determined based on the sum of the lengthsof edge M and edge F. The length of edge M may be determined by thelength feature value (for example, 200 m) in the features of edge M, andthe same is true for edge F.

In some embodiments, the second distance may be determined based on thelength sum of the edges corresponding to the shortest path connectingthe current node to be allocated and the previous target node. Theprevious target node may be a target node divided into the initialsecond pipeline network sub-graph in the previous round of sub-graphsegmentation processing at last. For example, node n7 is used as thetarget node in the previous round of sub-graph segmentation processing,and the node to be allocated in current round of sub-graph segmentationprocessing is node n5, then the second distance corresponding to node n5is the length of edge F.

In some embodiments, the smart gas network security management platform130 may further determine the initial second network sub-graph with thesmallest sub-graph complexity based on the sub-graph complexity of eachinitial second network sub-graph, and determine the priority value ofeach node to be allocated based on the first distance and the seconddistance of the each node to be allocated. The smaller the firstdistance, the larger the priority value. The larger the second distance,the larger the priority value. In some embodiments, the smart gaspipeline network security management platform 130 may determine thefirst priority value based on the first distance of the node to beallocated, determine the second priority value based on the seconddistance of the node to be allocated, and then determine the finalpriority value of the node to be allocated based on the average of thefirst priority value and the second priority value of the node to beallocated.

In some embodiments, the smart gas pipeline network security managementplatform 130 may sort (e.g., in descending order) the priority values ofthe nodes to be allocated, and select the node to be allocated with thelargest priority value as the target node.

In some embodiments of the present disclosure, the preset screeningmanner may help balance the complexity of each second pipeline networksub-graph, and prevent the sizes of the second pipeline networksub-graphs from being unbalanced.

Step S3, determining an initial second pipeline network sub-graph towhich the target node belongs based on a target function value of eachinitial second pipeline network sub-graph corresponding to the targetnode.

The target function value may be used to determine a probability valuethat the target node screened in step S2 is finally divided into acertain initial second pipeline network sub-graph in the at least oneinitial second pipeline network sub-graph. The target function value maybe related to the sub-graph complexity of the initial second pipelinenetwork sub-graph and the first distance between the target node and theinitial second pipeline network sub-graph.

In some embodiments, the target function may be a preset algorithm orformula. Exemplarily, the target function value F may bek1^(∗)d1+k2^(∗)d2, where, k1 and k2 are preset coefficients. Forexample, k1 is 0.5, k2 is 0.3. d1 is the sub-graph complexity, which mayrepresent the complexity of the initial second pipeline networksub-graph into which the target node is to be divided; and d2 is thefirst distance between the target node and the initial second pipelinenetwork sub-graph into which the target node is to be divided.

In some embodiments, the smart gas pipeline network security managementplatform 130 may determine the function value when the target node isdivided into each initial second pipeline network sub-graphcorrespondingly based on the foregoing manner and determine the initialsecond network sub-graph corresponding to the minimum target functionvalue F to which the target node is divided.

In some embodiments, the target function value may also be related tothe increment of the variance value of the inspection priority values ofnodes and edges in the initial second pipe network sub-graph after thetarget node is divided into the initial second pipe network sub-graph.The larger the increment of the variance value, the smaller the targetfunction value may be. For example, the initial target function valuemay be determined based on the foregoing manner, the adjustment value ofthe initial target function value may be determined based on theincrement of the variance value, and the target function value may befinally determined based on the initial target function value and theadjustment value.

It may be understood that the inspection priority values of each nodeand each edge in the initial second pipeline network sub-graph may bedifferent. With the increase of the rounds of iteration, the number ofthe nodes and edges in each initial second pipeline network sub-graphmay also increase. Correspondingly, the increment of the variance valueof the inspection priority values of each node and each edge of theinitial second pipeline network sub-graph of the current iteration andthe previous iteration may have a fluctuation. When the fluctuation issmaller, it indicates that the inspection priority values of each nodeand each edge in the initial second pipeline network sub-graph may becloser, the uncertainty in the subsequent actual inspection route orinspection sequence may be greater, and the error may also be larger.Otherwise, when the fluctuation is larger, it indicates that thedifference between the inspection priority values of each node and eachedge in the initial second pipeline network sub-graph may be larger, andthe subsequent determination of the actual inspection route or theinspection sequence may be clear, and the error may be smaller.

S4, determining a new target node and repeating the above operationsuntil the initial second pipeline network sub-graphs to which all thenodes to be allocated belong are determined.

The new target node may refer to a node to be allocated screened by thecurrent round of iterative sub-graph segmentation processing after theprevious round of sub-graph segmentation processing is completed. Forthe specific screening manner, please refer to the scheme of theaforementioned step S2.

In some embodiments, the smart gas pipeline network security managementplatform 130 may repeat the operations from step S1 to step S3 in eachround of iteration, and gradually divide the un-allocated nodes andedges in the second pipeline network graph into the initial secondpipeline network. Until all the nodes to be allocated in the secondpipeline network graph are allocated into the initial second pipelinenetwork sub-graph to which they belong, the iteration ends.

It should be noted that when two nodes to be allocated (such as nodes n2and n5 in FIG. 4 ) connected by an edge (such as edge D in FIG. 4 ) inthe second pipe network graph are allocated to two different second pipenetwork sub-graphs (such as sub-graph 422 and sub-graph 421 in FIG. 4 ),edge D may belong to both second pipe network sub-graphs. In this case,the smart gas pipeline network security management platform 130 may markor prompt the edge D. When inspection personnel inspects the inspectionsub-areas corresponding to sub-graph 422 and sub-graph 421, the gaspipeline corresponding to the edge D may be inspected by the inspectionpersonnel in the inspection sub-area corresponding to sub-graph 422 orsub-graph 421. In some embodiments, the smart gas pipeline networksecurity management platform 130 may also determine the sub-graph towhich the edge D belongs based on the direction of the edge D, such asallocating the edge D to the sub-graph where the arrow start point islocated, that is, the inspection personnel in the inspection sub-areacorresponding to the sub-graph 422 may be responsible for the inspectionof the edge D, etc. In some embodiments, other manners may also beadopted, such as random allocation, etc.

In step S5, using the each initial second pipeline network sub-graphafter foregoing operations are completed as a final second pipelinenetwork sub-graph for determining a corresponding inspection sub-area.

As shown in FIG. 4 , when the iteration is terminated, all nodes andedges in the second pipeline network graph 413 may be divided, and thenthe nodes and edges contained in the final three second pipeline networksub-graphs may be determined, such as the sub-graph 421, the sub-graph422, and the sub-graph 423.

It should be noted that the preset sub-graph segmentation manner mayinclude a segmentation processing by nodes, by edges, etc. Theabove-mentioned preset sub-graph segmentation manner takes thesegmentation processing by nodes as an example, which is not intended tobe limited.

Step 430, determining the at least one inspection sub-area based on theat least one second pipeline network sub-graph.

The inspection sub-area may refer to a physical inspection areacorresponding to the second pipeline network sub-graph.

In some embodiments, when the smart gas pipeline network securitymanagement platform 130 executes step 430, the second pipeline networksub-graph determined by the preset sub-graph segmentation manner may bedetermined as the inspection sub-area. For example, the smart gaspipeline network security management platform 130 may determine eachsecond pipeline network sub-graph in the final second pipeline networksub-graph as an inspection sub-area.

Specifically, as shown in FIG. 4 , the sub-graph 421, sub-graph 422, andsub-graph 423 correspond to three different inspection sub-areas. It maybe understood that each final second pipeline network sub-graphcorresponds to an actual gas pipeline network distribution.Correspondingly, referring to the final second pipe network sub-graph,the smart gas pipe network security management platform 130 can realizethe generation of inspection sub-areas by corresponding the pipe networkdevices (such as gas pipelines, gate stations, and gas storage stations)corresponding to the nodes or edges in each final second pipe networksub-graph to the actual area.

In some embodiments of the present disclosure, the second pipe networkdiagram may be automatically segmented by the preset sub-graphsegmentation manner, which can improve the efficiency of segmenting thesecond pipe network diagram. At the same time, the sub-graph complexitywhen dividing the second pipe network diagram may be taken intoconsideration, which can be conducive to a more balanced sub-graph ofthe divided second pipe network and a more balanced inspection workloadof the further determined inspection sub-area.

It should be noted that the above descriptions about process 200,process 300 and process 400 are only for example and illustration, anddo not limit the scope of application of the present disclosure. Forthose skilled in the art, various modifications and changes may be madeto the process under the guidance of the present disclosure. However,these corrections and modifications are still within the scope of thisdisclosure.

FIG. 5 is a schematic diagram illustrating an exemplary process fordetermining an inspection priority value of an inspection pointaccording to some embodiments of the present disclosure.

In some embodiments, each of the at least one inspection point may havean inspection priority value 530, and the inspection route may bedetermined based on the inspection priority value 530 of the each of theat least one inspection point.

The inspection priority value 530 may refer to a priority value forinspection personnel to an inspect inspection point. The inspectionpriority value 530 may be a value in the interval [0, 1], such as 0.3,0.8. The larger the inspection priority value of the inspection point,the more priority to perform an inspection on the inspection point.

In some embodiments, the inspection priority value 530 may be determinedbased on the pipeline features 510 corresponding to the inspectionpoint.

The pipeline features may refer to properties or information of gaspipeline. The pipeline features may include physical parameters of thegas pipeline, such as the material, length, thickness, and diameter ofthe gas pipeline, an area to which the gas pipeline belongs, etc. Thepipeline features may also include operating parameters of the gaspipeline, such as gas composition, gas flow rate, gas pressure level,and gas transmission frequency delivered by the gas pipeline, etc. Thepipeline features may also include other preset information, such ashistorical inspection times, failure rate, and maintenance records ofthe gas pipeline, etc.

In some embodiments, the smart gas pipeline network security managementplatform 130 may obtain the gas pipeline related information (e.g.,physical parameters, operating parameters, etc.) stored in the smart gasdata center 132, and determine the pipeline features based on the gaspipeline related information. In some embodiments, the pipeline featuresmay be represented in various forms, for example, the pipeline featuresmay be represented in forms including but not limited to vectors andvector matrixes. Exemplarily, the pipeline features may be representedby a vector of (a, b, c, d, e). The first element a of the vectorrepresents the inspection sub-area where the gas pipeline is located,the second element b represents the length of the gas pipeline, thethird element c represents the average daily gas flow, the fourthelement d represents the gas pressure level, and the fifth element erepresents the failure rate.

In some embodiments, the smart gas pipeline network security managementplatform 130 may determine the inspection priority value 530 of theinspection point through the feature determination model 520 based onthe pipeline features 510 corresponding to the inspection point.

The feature determination model 520 may refer to a model for determiningthe inspection priority value 530 of the inspection point. The featuredetermination model 520 may be a trained machine learning model. Forexample, the feature determination model 520 may include a recurrentneural network model, a convolutional neural network, or other custommodel structures, or the like, or any combination thereof.

As shown in FIG. 5 , the input of the feature determination model 520may include the pipeline features 510 corresponding to the inspectionpoint, and based on the processing of the feature determination model520, the inspection priority value 530 corresponding to the inspectionpoint may be output.

In some embodiments, the feature determination model 520 may be obtainedby training a large number of first training samples with labels. Thefirst training sample may include a sample pipeline historical featurevector constructed based on multiple sets of historical gas pipelinerelated information. The multiple sets of gas pipeline relatedinformation may be obtained based on historical data stored in the smartgas data center. The label of the first training sample may be theinspection priority value of the inspection point corresponding to thehistorical feature vector of each sample pipeline. The labels may belabeled based on manual labeling or other feasible manners.

When training the feature determination model 520, the smart gaspipeline network security management platform 130 may input the samplepipeline historical feature vector into the feature determination model520, construct a loss function based on the output of the featuredetermination model 520 and the label of the first training sample, anditeratively update the parameters of the initial feature determinationmodel based on the loss function until a preset condition is met and thetraining is completed, thus obtaining a trained feature determinationmodel. The preset condition may be that the loss function is smallerthan the threshold, converges, or the training cycle reaches thethreshold.

In some embodiments, the inspection priority value 530 of the inspectionpoint may be also related to the time and/or rounds when thecorresponding node and/or edge of the inspection point in the secondpipe network diagram 413 is divided into sub-graphs in sub-graphssegment.

It should be noted that the pipeline features 510 corresponding todifferent inspection points may be the same, and the inspection priorityvalue 530 output by the feature determination model 520 may be the same.In some embodiments, the inspection priority value 530 output by thefeature determination model 520 may be used as a candidate inspectionpriority value.

In some embodiments, the smart gas pipeline network security managementplatform 130 may determine the final inspection priority value 530 ofthe inspection point based on the time and/or rounds 540 when theinspection point is divided into the second network sub-graph and theaforementioned candidate inspection priority value through presetinspection rules. The time and/or round 540 when the inspection point isdivided into the second pipeline network sub-graph may be obtained basedon the record of the iteration counter during the iteration of thesub-graph segmentation manner.

In some embodiments, a weight coefficient of the candidate inspectionpriority value may also be set, and the weight coefficient may beinversely proportional to the time and/or rounds when the inspectionpoint is divided into the second pipeline network sub-graph.Exemplarily, the time and/or rounds when the inspection points aredivided into the second pipeline network sub-graph is t, the weightcoefficient k is 1/t, and the final inspection priority value V is k*V′;and V′ is a candidate inspection priority value output by thefeature-based determination model 520.

Exemplarily, as shown in FIG. 4 , the inspection personnel is located atnode n7 currently, and may go to node n5 or node n8. If the pipelinefeatures of node n5 and node n8 are exactly the same and the node n8 isfirst selected as the node to be allocated and allocated to thesub-graph 421 in the iterative processing of the sub-graph segmentationmanner, the node n8 may have a higher inspection priority value. At thistime, the inspection route of the inspection personnel may be a routefrom node n7 to node n8, that is, the pipeline corresponding to edge L.

It may be understood that by introducing the time and/or rounds 540 whenthe inspection point is divided into the second pipe network sub-graph,when the inspection priority values 530 of the inspection pointsdetermined based on the pipeline features are the same in the samesecond pipe network sub-graph, the inspection points that are firstdivided into the second pipe network sub-graph can have higherinspection priority, thus eliminating the uncertainty of the inspectionorder.

In some embodiments of the present disclosure, determining theinspection priority value of the inspection point by the featuredetermination model, the inspection priority value of the inspectionpoint can be determined automatically and in real time, which canimprove efficiency. At the same time, introducing the time and/or roundswhen the inspection point is divided into the sub-graph can eliminatethe interference of the inspection priority order caused by the samepipeline features, which can make the inspection order of the inspectionpoints clearer.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and “some embodiments” mean that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution, e.g., an installation on an existing server or mobiledevice.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, numbers describing the number of ingredients andattributes are used. It should be understood that such numbers used forthe description of the embodiments use the modifier “about”,“approximately”, or “substantially” in some examples. Unless otherwisestated, “about”, “approximately”, or “substantially” indicates that thenumber is allowed to vary by ±20%. Correspondingly, in some embodiments,the numerical parameters used in the description and claims areapproximate values, and the approximate values may be changed accordingto the required characteristics of individual embodiments. In someembodiments, the numerical parameters should consider the prescribedeffective digits and adopt the method of general digit retention.Although the numerical ranges and parameters used to confirm the breadthof the range in some embodiments of the present disclosure areapproximate values, in specific embodiments, settings of such numericalvalues are as accurate as possible within a feasible range.

For each patent, patent application, patent application publication, orother materials cited in the present disclosure, such as articles,books, specifications, publications, documents, or the like, the entirecontents of which are hereby incorporated into the present disclosure asa reference. The application history documents that are inconsistent orconflict with the content of the present disclosure are excluded, andthe documents that restrict the broadest scope of the claims of thepresent disclosure (currently or later attached to the presentdisclosure) are also excluded. It should be noted that if there is anyinconsistency or conflict between the description, definition, and/oruse of terms in the auxiliary materials of the present disclosure andthe content of the present disclosure, the description, definition,and/or use of terms in the present disclosure is subject to the presentdisclosure.

Finally, it should be understood that the embodiments described in thepresent disclosure are only used to illustrate the principles of theembodiments of the present disclosure. Other variations may also fallwithin the scope of the present disclosure. Therefore, as an example andnot a limitation, alternative configurations of the embodiments of thepresent disclosure may be regarded as consistent with the teaching ofthe present disclosure. Accordingly, the embodiments of the presentdisclosure are not limited to the embodiments introduced and describedin the present disclosure explicitly.

What is claimed is:
 1. A method for smart gas pipeline networkinspection, implemented on a smart gas pipeline network securitymanagement platform based on an Internet of Things system for smart gaspipeline network inspection, and the method comprising: obtaining a gaspipeline network distribution; determining at least one inspectionsub-area based on the gas pipeline network distribution; anddetermining, based on the at least one inspection sub-area, aninspection plan for each of the at least one inspection sub-area, theinspection plan at least including an inspection frequency.
 2. Themethod according to claim 1, wherein the Internet of Things system forsmart gas pipeline network inspection further includes a smart gas userplatform, a smart gas service platform, a smart gas sensor networkplatform, and a smart gas object platform; the smart gas object platformis configured to obtain the gas pipeline network distribution, andtransmit the gas pipeline network distribution to the smart gas pipelinenetwork security management platform through the smart gas sensornetwork platform; and the method further includes: feeding back theinspection plan to the smart gas user platform based on the smart gasservice platform.
 3. The method according to claim 1, wherein the smartgas user platform includes a gas user sub-platform and a supervisionuser sub-platform; the smart gas service platform includes a smart gasconsumption service sub-platform corresponding to the gas usersub-platform and a smart supervision service sub-platform correspondingto the supervision user sub-platform; a smart gas pipeline networksecurity management platform includes a smart gas pipeline networkinspection management sub-platform and a smart gas data center; whereinthe smart gas pipeline network inspection management sub-platformincludes an inspection plan management module, an inspection timewarning module, an inspection status management module, and aninspection problem management module; the smart gas sensor networkplatform includes a smart gas pipeline network device sensor networksub-platform and a smart gas pipeline network inspection engineeringsensor network sub-platform; and the smart gas object platform includesa smart gas pipeline network device object sub-platform and a smart gaspipeline network inspection engineering object sub-platform.
 4. Themethod according to claim 1, wherein the determining at least oneinspection sub-area based on the gas pipeline network distributionincludes: determining at least one inspection personnel station based onthe gas pipeline network distribution; and determining the at least oneinspection sub-area based on the gas pipeline network distribution andthe at least one inspection personnel station.
 5. The method accordingto claim 4, wherein the determining at least one inspection personnelstation based on the gas pipeline network distribution includes:constructing a first pipeline network graph based on the gas pipelinenetwork distribution; wherein nodes of the first pipeline network graphcorrespond to pipeline network branches in the gas pipeline networkdistribution; edges of the first pipeline network graph correspond topipelines in the gas pipeline network distribution; and each edge of thefirst pipeline network graph corresponds to a pipeline connecting twopipeline network branches; outputting, based on the nodes of the firstpipeline network graph and/or the edges of the first pipeline networkgraph through a probability determination model, a probability that thenodes of the first pipeline network graph and/or the edges of the firstpipeline network graph are inspection personnel stations; anddetermining the at least one inspection personnel station based on anoutput of the nodes of the first pipeline network graph and the edges ofthe first pipeline network graph.
 6. The method according to claim 5,wherein the determining the at least one inspection sub-area based onthe gas pipeline network distribution and the at least one inspectionpersonnel station includes: constructing a second pipeline network graphbased on the gas pipeline network distribution and the at least oneinspection personnel station; wherein nodes of the second pipelinenetwork graph include pipeline network nodes and station nodes; thepipeline network nodes correspond to the pipeline network in the gaspipeline network distribution; the station nodes correspond to the atleast one inspection personnel station; and edges of the second pipelinenetwork graph correspond to the pipelines in the gas pipeline networkdistribution; determining at least one second pipeline network sub-graphby a preset sub-graph segmentation manner based on the second pipelinenetwork graph; and determining the at least one inspection sub-areabased on the at least one second pipeline network sub-graph.
 7. Themethod according to claim 6, wherein the preset sub-graph segmentationmanner includes: determining at least one initial second pipelinenetwork sub-graph based on the station nodes of the second pipelinenetwork graph; wherein each of the at least one initial second pipelinenetwork sub-graph includes a station node; using the pipeline networknodes of the second pipeline network graph as nodes to be allocated, andselecting a target node from the nodes to be allocated based on a presetscreening manner; determining an initial second pipeline networksub-graph to which the target node belongs based on a target functionvalue of each initial second pipeline network sub-graph corresponding tothe target node; determining a new target node and repeating aboveoperations until the initial second pipeline network sub-graphs to whichall the nodes to be allocated belong are determined; and using the eachinitial second pipeline network sub-graph after foregoing operations arecompleted as a final second pipeline network sub-graph for determining acorresponding inspection sub-area.
 8. The method according to claim 1,wherein the inspection plan further includes an inspection route; theinspection sub-area includes an inspection personnel station and atleast one inspection point; and the determining an inspection plan foreach of the at least one inspection sub-area includes: determining aroute that traverses each of the at least one inspection point from theinspection personnel station in the inspection sub-area as theinspection route; and determining the inspection plan of the inspectionsub-area based on the inspection route.
 9. The method according to claim8, wherein the each inspection point in the at least one inspectionpoint has an inspection priority value; the inspection route isdetermined based on the inspection priority value of the each inspectionpoint; and the inspection priority value is determined based on pipelinefeatures corresponding to the inspection point.
 10. The method accordingto claim 9, wherein the inspection priority value is determined based onpipeline features corresponding to the inspection point includes:determining the inspection priority value of the inspection pointthrough a feature determination model based on the pipeline featurescorresponding to the inspection point.
 11. An Internet of Things systemfor smart gas pipeline network inspection, comprising: a smart gas userplatform, a smart gas service platform, a smart gas pipeline networksecurity management platform, a smart gas sensor network platform, and asmart gas object platform; wherein the smart gas object platform isconfigured to obtain a gas pipeline network distribution, and transmitthe gas pipeline network distribution to the smart gas pipeline networksecurity management platform through the smart gas sensor networkplatform; the smart gas pipeline network security management platform isconfigured to: determine at least one inspection sub-area based on thegas pipeline network distribution; determine, based on the at least oneinspection sub-area, an inspection plan for each of the at least oneinspection sub-area, the inspection plan at least including aninspection frequency; and the smart gas service platform is configuredto feed back the inspection plan to the smart gas user platform.
 12. TheInternet of Things system according to claim 11, wherein the smart gasuser platform includes a gas user sub-platform and a supervision usersub-platform; the smart gas service platform includes a smart gasconsumption service sub-platform corresponding to the gas usersub-platform and a smart supervision service sub-platform correspondingto the supervision user sub-platform; a smart gas pipeline networksecurity management platform includes a smart gas pipeline networkinspection management sub-platform and a smart gas data center; whereinthe smart gas pipeline network inspection management sub-platformincludes an inspection plan management module, an inspection timewarning module, an inspection status management module, and aninspection problem management module; the smart gas sensor networkplatform includes a smart gas pipeline network device sensor networksub-platform and a smart gas pipeline network inspection engineeringsensor network sub-platform; and the smart gas object platform includesa smart gas pipeline network device object sub-platform and a smart gaspipeline network inspection engineering object sub-platform.
 13. TheInternet of Things system according to claim 11, wherein the smart gaspipeline network security management platform is further configured to:determine at least one inspection personnel station based on the gaspipeline network distribution; and determine the at least one inspectionsub-area based on the gas pipeline network distribution and the at leastone inspection personnel station.
 14. The Internet of Things systemaccording to claim 13, wherein the smart gas pipeline network securitymanagement platform is further configured to: construct a first pipelinenetwork graph based on the gas pipeline network distribution; whereinthe nodes of the first pipeline network graph correspond to pipelinenetwork branches in the gas pipeline network distribution; edges of thefirst pipeline network graph correspond to pipelines in the gas pipelinenetwork distribution; and each edge of the first pipeline network graphcorresponds to a pipeline connecting two pipeline network branches;output, based on the nodes of the first pipeline network graph and/orthe edges of the first pipeline network graph through a probabilitydetermination model, a probability that the nodes of the first pipelinenetwork graph and/or the edges of the first pipeline network graph areinspection personnel stations; and determine the at least one inspectionpersonnel station based on an output of the nodes of the first pipelinenetwork graph and the edges of the first pipeline network graph.
 15. TheInternet of Things system according to claim 14, wherein the smart gaspipeline network security management platform is further configured to:construct a second pipeline network graph based on the gas pipelinenetwork distribution and the at least one inspection personnel station;wherein nodes of the second pipeline network graph include pipelinenetwork nodes and station nodes; the pipeline network nodes correspondto the pipeline network in the gas pipeline network distribution; thestation nodes correspond to the at least one inspection personnelstation; and edges of the second pipeline network graph correspond tothe pipelines in the gas pipeline network distribution; determine atleast one second pipeline network sub-graph by a preset sub-graphsegmentation manner based on the second pipeline network graph; anddetermine the at least one inspection sub-area based on the at least onesecond pipeline network sub-graph.
 16. The Internet of Things systemaccording to claim 15, wherein the preset subgraph segmentation mannerincludes: determining at least one initial second pipeline networksub-graph based on the station nodes of the second pipeline networkgraph; wherein each of the at least one initial second pipeline networksub-graph includes a station node; the pipeline network node of thesecond pipeline network graph as nodes to be allocated, and selecting atarget node from the nodes to be allocated based on a preset screeningmanner; determining an initial second pipeline network sub-graph towhich the target node belongs based on a target function value of eachinitial second pipeline network sub-graph corresponding to the targetnode; determining a new target node and repeating above operations untilthe initial second pipeline network sub-graphs to which all the nodes tobe allocated belong are determined; and using the each initial secondpipeline network sub-graph after foregoing operations are completed as afinal second pipeline network sub-graph for determining a correspondinginspection sub-area.
 17. The Internet of Things system according toclaim 11, wherein the inspection plan further includes an inspectionroute; the inspection sub-area includes an inspection personnel stationand at least one inspection point; and the smart gas pipeline networksecurity management platform is further configured to: determine a routethat traverses each of the at least one inspection point from theinspection personnel station in the inspection sub-area as theinspection route; and determining the inspection plan of the inspectionsub-area based on the inspection route.
 18. The system of claim 17,wherein the each inspection point in the at least one inspection pointhas an inspection priority value; the inspection route is determinedbased on the inspection priority value of the each inspection point; andthe inspection priority value is determined based on pipeline featurescorresponding to the inspection point.
 19. The Internet of Things systemaccording to claim 18, wherein the smart gas pipeline network securitymanagement platform is further configured to: determine the inspectionpriority value of the inspection point through a feature determinationmodel based on the pipeline features corresponding to the inspectionpoint.
 20. A non-transitory computer-readable storage medium storingcomputer instructions, wherein after reading the computer instructionsin the storage medium, a computer executes the method for smart gaspipeline network inspection according to claim 1.